As an example of an art which generates a restored image from a deteriorated image, the super resolution art is known.
A method, which uses a dictionary created by learning a case in which a low resolution image and a high resolution image are associated each other, is called the learning type super resolution art in particular among the super resolution arts. Here, the dictionary is a dictionary which is created by learning the case in which the low resolution image and the high resolution image are associated each other.
An example of the learning type super resolution art is disclosed in a non-patent document 1. According to the learning type super resolution art which is disclosed in the non-patent document 1, the following method (here, referred to as a super resolution process) is carried out.
Firstly, the super resolution process receives an input image which is a low resolution image.
Moreover, the super resolution process generates a low frequency component from the input image.
The super resolution process cuts out a low frequency patch from the generated low frequency component, and calculates an amount of characteristics of low frequency on the basis is of the low frequency patch.
The super resolution process searches the dictionary for plural pieces of learning data on amount of characteristics of low frequency in an order of short distance which is from the calculated amount of characteristics of low frequency. Then, the super resolution process reads an amount of characteristics of high frequency which is associated with the searched learning data on amount of characteristics of low frequency.
Then, the super resolution process selects one amount of characteristics of high frequency on the basis of a distance when searching, consistency with the adjacent high frequency block, a concurrent probability of the amount of characteristics of low frequency and the amount of characteristics of high frequency which are learned separately in a learning step, or the like.
The art, which is described in the non-patent document 1, restrains a memory size, and makes a calculation cost reduced by adopting structure that the dictionary has one to many correspondence which means that plural amounts of characteristics of low frequency similar each other are grouped under one representative.
For example, a patent document 1 discloses an example of a super resolution apparatus.
The super resolution apparatus, which is described in the patent document 1, includes N times enlargement unit, a high pass filter unit, a patch extraction unit, an addition unit and a learning database.
The N times enlargement unit generates an enlargement image from a low resolution input image.
The high pass filter unit generates a middle frequency image from the enlargement image.
The patch extraction unit generates an estimation patch from the middle frequency image, a learning middle frequency patch, and a learning high frequency patch.
The addition unit generates an output image by adding the enlargement image and the estimation patch.
The learning database outputs the learning middle frequency patch and the learning high frequency patch.
The patent document 1 discloses that, in the above-mentioned super resolution apparatus, a process unit is not limited to a rectangular block, and may be in any form such as a circle, a polygon, or the like.